As computer devices continue to decrease in size and increase in capacity and capability, the minimum device geometries used to manufacture the components continues to decrease. The decreases in device geometries enables continued system scaling by enabling improved performance with lower power consumption. However, the decreasing geometries create component-level issues. For example, the decreasing geometries that enable memories to increase in capacity and access speeds exacerbate row hammer or row disturb issues. “Row hammer” refers to a failure caused by repeated access to a target row or aggressor row within a time period. The failure is actually in a victim row that is adjacent or proximate to the target/aggressor row, where repeated activation of the target row causes migration of charge across the passgate of the victim row, resulting in a nondeterministic state of the victim row. Row hammer is a known issue in DRAM (dynamic random access memory) devices.
Row hammer mitigation managed by the memory controller is increasingly impractical because of amount of data that would either needed to be managed by the memory controller or exchanged between the memory controller and DRAM devices. Mitigation within the DRAM device itself typically involves the DRAM device tracking row hammer or performing heuristic operation to address row hammer. However, with the decreasing device geometries, the number of activates to a specific row that could cause a row hammer event has gone from 500K to 300K, and is now projected to be at 100K and even decrease to a range of about 30K-50K activates. With fewer activates needed to cause a row hammer event, more rows could be aggressors within the refresh window.
Thus, the complexity of row hammer mitigation increases as the number of activates decreases. Therefore, traditional row hammer mitigation techniques in the DRAM device are unlikely to scale to future devices, and more refreshes will be needed to address row hammer. Additionally, when all DRAM devices of a group (e.g., all devices in a rank) apply the same mitigation algorithm, they will refresh the same potential victim rows, which could starve out more and more victim rows resulting in data loss.
Descriptions of certain details and implementations follow, including non-limiting descriptions of the figures, which may depict some or all examples, and well as other potential implementations.